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Posted to issues@spark.apache.org by "Yanbo Liang (JIRA)" <ji...@apache.org> on 2015/12/01 11:48:10 UTC
[jira] [Comment Edited] (SPARK-8519) Blockify distance computation
in k-means
[ https://issues.apache.org/jira/browse/SPARK-8519?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15033496#comment-15033496 ]
Yanbo Liang edited comment on SPARK-8519 at 12/1/15 10:47 AM:
--------------------------------------------------------------
There are two issues that I should confirm before start coding:
* We will implement this optimization still at MLlib side and called by ML.
* We will remove "runs" at MLlib side at Spark 1.7, it means I can ignored this parameter.
[~mengxr]
was (Author: yanboliang):
There are two issues that I should confirm before start coding:
* We will implement this optimization still at MLlib side and called by ML.
* We will remove "runs" at MLlib side at Spark 1.7, it means I can ignored this parameter.
[~mengxr]
> Blockify distance computation in k-means
> ----------------------------------------
>
> Key: SPARK-8519
> URL: https://issues.apache.org/jira/browse/SPARK-8519
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Affects Versions: 1.4.0
> Reporter: Xiangrui Meng
> Labels: advanced
>
> The performance of pairwise distance computation in k-means can benefit from BLAS Level 3 matrix-matrix multiplications. It requires we update the implementation to use blocks. Even for sparse data, we might be able to see some performance gain.
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